Implementasi Algoritma Gaussian Naïve Bayes Dalam Klasifikasi Status Gizi Pada Balita
- Hery Kurniawan UMKT, Indonesia
-
Abdul Rahim
*
Universitas Muhammadiyah Kalimantan Timur,
Indonesia
- Taghfirul Azhima Yoga Siswa Universitas Muhammadiyah Kalimantan Timur, Indonesia (*) Corresponding Author
Abstract
Nutritional status is a condition related to nutrition that can be measured and results from the balance between the body's nutritional needs and nutrient intake from food. In Indonesia, nutritional problems such as malnutrition and other nutritional issues are still prevalent. In this context, the use of machine learning (ML) and data mining (DM) techniques and tools can be very helpful in tackling challenges in the manufacturing sector. Therefore, this study will use the Naïve Bayes Classifier algorithm with a Gaussian model. The data used is the nutritional status data of toddlers from January to July 2023 in Samarinda City. The attributes in this study include Gender, Birth Weight, Birth Height, Age at Measurement, Body Weight, Body Height, ZS BW/A, BW/A, ZS BH/A, and BH/A. The determination of toddlers' nutritional status in this study is based on the BW/BH index, which consists of 6 classes: severe malnutrition, undernutrition, good nutrition, risk of overnutrition, overnutrition, and obesity. From the study conducted, it was found that the Naïve Bayes Classifier algorithm with the Gaussian model can accurately classify toddlers' nutritional status. From the data processing performed, it was found that the accuracy value of the Gaussian model is 81.85%.
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